Apparatus and method for processing image based on layers

Abstract

An apparatus and method for processing an image based on layers. The apparatus includes: an image divider dividing an image into E layers, each layer having at least one block, e being an positive integer at least equal to 2; and first through E-th layer basis matrix generators respectively generating first through E-th layer basis matrices using the divided image and outputting a set of the first through E-th layer basis matrices as a final basis matrix, wherein the e-th (1≦e≦E) layer basis matrix generator, with respect to each block included in the e-th layer, generates a block model using a kernel matrix obtained by local feature analysis, multiplies a zero mean matrix generated from the divided image by the result of transposing the block model, calculates a between-class scatter matrix and a within-class scatter matrix by linear discriminant analysis using the multiplied result, calculates a discriminant transformation matrix using the calculated between-class scatter matrix and the calculated within-class scatter matrix, multiplies the discriminant transformation matrix by the block model, outputs the multiplied result as a subbasis matrix, and outputs a set of subbasis matrices generated in all of the blocks included in the e-th layer as the e-th layer basis matrix.

Description

CROSS-REFERENCE TO RELATED APPLICATION [0001] This application claims the benefit of Korean Patent Application No. 10-2004-0098147, filed on Nov. 26, 2004, in the Korean Intellectual Property Office, the disclosure of which is incorporated herein by reference. BACKGROUND OF THE INVENTION [0002] 1. Field of the Invention [0003] The present invention relates to an apparatus and method for processing an image for facial recognition used in a biometrics, video surveillance, or multimedia retrieval system etc. as an essential technology, and more particularly, to an apparatus and method for processing an image based on layers. [0004] 2. Description of Related Art [0005] Recently, a variety of methods for improving the performance of facial recognition have been suggested. One of the conventional methods, local feature analysis (LFA), has been introduced by P. S. Penev and J. J. Atick [“Local Feature Analysis: A General Statistical Theory for Object Representation,” Network: Communication...

AI Technical Summary

Problems solved by technology

Sparsification used in reducing dimension of an image and a correlation of values obtained by LFA is performed to reduce a reconstruction error instead of improving discrimination of a facial model and thus, there is a limitation in the method.

In the conventional method, since selection of features is designed not to improve discrimination of a facial model but to minimize a reconstruction error, a structural problem still remains.

As another conventional method, local analysis, for example, component analysis shows only local characteristics, and thus, a local minimum problem may occur.

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